This paper investigates the use of Higher Order Neural Networks using a number of architectures to forecast the Gasoline Crack spread. The architectures used are Recurrent Neural Network and Higher Order Neural Networks; these are benchmarked against the standard MLP model. The final models are judged in terms of out-of-sample annualised return and drawdown, with and without a number of trading filters.
The results show that the best model of the spread is the recurrent network with the largest out-of-sample returns before transactions costs, indicating a superior ability to forecast the change in the spread. Further the best trading model of the spread is the Higher Order Neural Network with the threshold filter due a superior in- and out-of-sample risk/return profile.